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Table A1: Overview of the 15 projects reviewed in this report
Ghana
Côte d'Ivoire
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In this section, we analyse general information about the design and set-up of 15 different Child Labour Monitoring and Remediation Systems in the cocoa sector, 10 in Côte d’Ivoire and 5 in Ghana. The analysis is based on information shared with ICI by CLMRS implementers, and information on ICI-implemented CLMRS. In some cases we refer to more than one CLMRS project, when the same implementer has CLMRS in both Ghana and Côte d’Ivoire, or along different company supply chains, with each CLMRS project being adjusted to the specific context.
Components of CLMRS
In this section, we describe differences and similarities in system design and set-up. This section is organised around the following key components of a CLMRS, as defined in ICI’s Effectiveness Review of CLMRS in the Smallholder Agricultural Sector of Sub-Saharan Africa (2017):
Training and awareness-raising: monitoring agents receive specific training in monitoring techniques, child labour and child rights, and child safeguarding. The system includes an element of awareness-raising in the communities where children are monitored.
Monitoring: the system involves direct observations (involving a personal visit to home or farm), in order to identify child labourers and to determine risks to which they are exposed regularly (at a minimum frequency), as well as management of data in such a way that individual cases can be tracked over time.
Identification: the system identifies children in child labour or hazardous child labour (not just “at risk”), according to an operationalised definition, typically based on ILO conventions and national legislation, and a set of methods and tools.
Support (remediation and prevention): the system provides some form of remediation to children identified in (hazardous) child labour.
Follow-up: the system includes procedures for regular and repeated assessment of whether a child identified continues to be in (hazardous) child labour. This process included (i) personal visits and (ii) a clear procedure to declare that a child is no longer in hazardous child labour.
Third-party verification: data collected by the system is externally and independently verified/ audited to ensure that the information provided is correct and truthfully reflects the local situation.
Partnership: the system is implemented in coordination with different structures and institutions involved in addressing child labour (national to local government bodies, workers’ and employers’ organisations, certification schemes, industry, etc) and shares information.
This overview describes the features of many CLMRS commonly implemented by civil society and private sector actors. However, at national level, the Ghana Child Labour Monitoring System (GCLMS50) matches very closely the systems covered here. The main differences at the operational level are the systematic use of referral to public services for remediation, rather than through direct intervention by the system, and the fact that follow-up is the responsibility of the public services and community actors involved in the system. In Côte d’Ivoire, the national child labour monitoring and remediation system, SOSTECI, has not only a direct operational function, but also a coordination role between systems implemented by various actors.
Training and awareness-raising
Training of monitoring agents
Training includes a mixture of topics: child rights, child labour and safeguarding, interview and awareness-raising techniques, use of digital monitoring tools, and the structure and functioning of the supply chain.
Among the CLMRS reviewed for which information on training duration was available, the minimum number of training days each agent should receive, according to the CLMRS protocol, ranges from 1 to 10. Figure A2 gives an overview of the variations of the amount of training given to agents among some of the projects:
Mandatory refresher training also varies greatly from none to annual sessions, the most common response.
Awareness-raising
Awareness-raising about child labour and the resulting harm, as well as other related topics may be addressed to farmers, local authorities, cooperatives / producer organisations or communities. The following table displays the number of farmers reportedly reached by individual or collective awareness-raising sessions since the project start, according to the information shared by the different CLMRS implementers.
Project | Household |
A | 120'000 |
B | 2'546 |
C | 23'543 |
D | 14'2875 |
E | 11'520 |
F | 65'625 |
G | 2'370 |
H | 3'642 |
I | 4'857 |
J | 830 |
K | 1'099 |
L | 168 |
Note:Letter labels are arbitrarily assigned to the CLMRS projects, with new letters assigned in each table.
Monitoring
In the context of a CLMRS, monitoring consists of direct observations in the field (personal visits to homes and/or farms), identification of children in child labour (or at risk), and management of data collected in such a way that individual cases can be tracked over time.
Monitoring agents
The profile of monitoring agents may influence the amount of training they need to carry out data collection activities in the field.
Agents can be local cocoa producers or members a cooperative, who work part-time as monitors (from the community, known by the farmers); full-time external agents hired by cooperatives or trading companies with roles exceeding CLMRS (certification, farmer agricultural training) and covering several communities (from outside the community, but known by the farmers); external enumerators hired specifically for CLMRS data collection (from outside the community, unknown to the farmers); or community members who do the monitoring on a voluntary basis. Figure A3 provides an overview of the types of monitoring agents used by the 15 CLMRS projects reviewed.
The large majority of agents are paid rather than unpaid....
Means of transport available to data collection agents
The total coverage of the different CLMRS reviewed varies from 4,700 to 75,000 households, which represents between 6 and 46 households per agent. The intended frequency of the household visits varies from every 2 years to 3 times a year.
Given the variations in the expected frequency of the visits and number of households to be covered by agent per year, the means of transportation available to the agents is clearly an important factor affecting an agent’s capacity to reach the households they monitor, especially in rural contexts where many villages are isolated and not accessible by paved roads.
Bicycles were the most common means of transportation for monitoring agents and were provided as part of the CLMRS in 10 projects.
Data collection tools
The large majority of CLMRS rely on mobile data collection, using a mixture of applications developed internally or by a third party. Two CLMRS make partial or exclusive use of paper-based questionnaires.
The questionnaires used to identify children in child labour vary among the projects. One project uses a questionnaire developed under the government child labour monitoring system, and the other projects use questionnaires developed in-house or by a third party specifically for the CLMRS.
Who provides information on child labour as part of the system?
In all projects reviewed, monitoring agents collect data from farmers or heads of the farming households. In most projects, at least one module of the questionnaires is addressed directly to children (11 projects). Only a few projects collect data from other household members, non-family farm workers or teachers (figure A4).
Note: Vertical axis displays the number of times a given type of informant is solicited throughout the CLMRS projects' data collection processes.
Identification of children in child labour, hazardous child labour or at risk
The identification process relies on having an operational definition of a child in child labour, in hazardous child labour, or at risk. The definitions used vary among projects, whereby all CLMRS rely on the relevant ILO Conventions and the national legislative frameworks for hazardous activities. For the category of children “at risk” of child labour, no common definition exists, and various CLMRS implementers have set up their own operational definitions. Examples of criteria for children to be considered “at risk” include: children identified in light work, without a birth certificate, out of school, with siblings or friends involved in hazardous child labour, with poor academic performance, poor school attendance, out of school and orphaned.
Response: withdrawal, referral and remediation
Projects offer a wide range of child labour remediation to children, households and communities, covering different intervention areas: some are intended to facilitate children’s access to quality education (such as provision of scholarships, school kits, birth certificates or bicycles to children) or to improve education infrastructure in the community; some aim to improve household income (such as income generating activity support, VSLAs, literacy training for adults); some are intended to provide alternatives to children’s engagement in hazardous tasks (such as mobilising community service groups, providing wheelbarrows, providing access to drinking water sources); and some relate to child protection or child rights more broadly (such as registration for Ghana’s National Health Insurance System (NHIS), vaccinations, setting up child protection committees).
Figure A5 provides an overview of the types of remediation and of how many of the reviewed CLMRS provide each type of remediation.
These interventions may be carried out by structures set up by the project, by local partners (NGOs, ICI, other civil society actors), by cooperatives / producer organisations, by community-based services supported by the project or by private company field staff. Alternatively, aiming to achieve increased sustainability of the remediation component of a CLMRS and to directly involve and strengthen government agencies, a few CLMRS projects refer identified cases of child labour to government services for remediation.
Follow-up of children identified in child labour
CLMRS include procedures to assess whether a child identified is still in (hazardous) child labour, namely: (i) personal visits and (ii) a clear procedure for declaring that a child is no longer in (hazardous) child labour.
The criteria used to declare that a child has stopped working or doing hazardous tasks differ across systems, but they mostly converge on the idea that the criteria should involve a certain time lag after the identification of the case and a certain number of follow-up visits. Examples of criteria include:
- Two follow-up visits over 9 months.
- Child follow-up at home and school + farm visit after 6 months of remediation.
- Mix of weekly and monthly visits over 6 months.
- Follow-up visits 3–6 months after remediation.
Third-party verification
CLMRS projects sometimes resort to external and independent entities to verify/audit their (anonymised) data, in order to make sure that information provided is correct and truthfully reflects the local situation. Four projects in the review resort to a third-party verification of their data on a regular basis. Among these, two projects made audit reports publicly available through the verifying entity.
Partnership
CLMRS also differ by the degree of cooperation embedded at several levels of the system. In some cases, one entity provides the IT infrastructure, manages data collection, data storage and data analysis for CLMRS projects in different supply chains; in other cases, various partners, including government offices, are involved at different levels of the CLMRS. Table A2 provides an overview of the number of companies involved in different kinds of collaboration:
Types of cooperation | Community | External IT providers | Local authorities involved in monitoring and remediation | International research institute involved in evaluation | NGOs or Foundations involved in remediation | Third-party data verification |
---|---|---|---|---|---|---|
Number of instances | 9 | 2 | 2 | 1 | 2 | 4 |
All projects included in this review are partially or totally funded by private sector resources, with one model also relying on resources mobilised from communities, while other projects are funded by international development assistance and private foundations.
Conclusions
The information provided by implementers of the different projects reviewed demonstrates a great diversity of set-ups and approaches to putting in place a Child Labour Monitoring and Remediation System. Beyond certain commonalities and the shared focus on tackling child labour, even within the limited scope of the cocoa sector in West Africa, approaches differ considerably.
The diversity of approaches CLMRS makes it challenging to compare these different systems; there is therefore no one-size-fits-all way to assess them and to report on their effectiveness.
Nonetheless, if we compare the information provided by CLMRS implementers in this effectiveness review with the results of the first phase, published in 2017, considerable progress has been made. In addition to the notable improvements in the coverage of CLMRS, there appears to be increasing alignment in terms of approaches taken on some aspects of system set-up and implementation, as well as calls from many CLMRS implementers for further standardisation of definitions and benchmarks.51
The diversity of approaches CLMRS makes it challenging to compare these different systems; there is therefore no one-size-fits-all way to assess them and to report on their effectiveness.
Core components of CLMRS | Instances observed in this review | Main trends |
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Training and awereness-raising |
|
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Monitoring |
|
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Identification |
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Support (prevention and prevention) |
|
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Follow-up |
|
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Third-party verification |
|
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Partnership |
|
|
References
52 ICI and other stakeholders have started developing data-based models for classifying children according to their child labour risk, using readily available child labour household data to train the models and farmer registers with basic demographic information. For an introduction to these approaches, see ICI (2021): Risk Models to Predict (Hazardous) Child Labour.