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Children end up in child labour as a result of many, often unknown or hidden, interactions between multiple actors and multiple factors within households, communities, and labour systems, leading to unpredictable outcomes for children and other sector stakeholders and sometimes resulting in the worst forms of child labour (WFCL). It is a complex problem, and interventions aimed at tackling it are also, inevitably, complex and challenging. The way they influence change is non-linear, causality is uncertain, and unintended consequences may result. Programmes such as the Child Labour: Action-Research-Innovation in South and South-Eastern Asia (CLARISSA) that are engaging with such intractable challenges and aim to reach the most left behind (children in WFCL) are operating in conditions of complexity. This complexity poses significant challenges to the way programmes are designed, planned, implemented, and evaluated, and requires a move away from linear and predetermined models. In this Working Paper, we share our experience and early learning about how to design and implement monitoring, evaluation and learning that intentionally embraces the challenge of complexity.

Accepted: 2020-06-25T14:55:41Z