Peer-reviewed article
Beyond the Walls: Patterns of Child Labour, Forced Labour, and Exploitation in a New Domestic Workers Dataset
Zoe Trodd, Catherine E. Waite, James Goulding, Doreen S. Boyd ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study analysed survey data from 11,759 domestic workers in India to identify patterns of child labour, forced labour, and exploitation. The research reveals characteristics of this hidden population and documents forms of exploitation including below-minimum-wage work. The study demonstrates how large-scale survey data can help measure and understand modern slavery and labour exploitation affecting vulnerable workers.
Key points
- The dataset of 11,759 domestic workers is the largest single survey of this population to date
- India is estimated to have 11 million people living in forms of contemporary slavery
- The analysis identified child labour, indicators of forced labour, and exploitation patterns including subminimum-wage work
- The study uses bivariate analysis, factor analysis, and spatial analysis to identify patterns
- The research demonstrates the value of 'found data' and participatory and citizen science approaches for measuring labour exploitation
Limitations
This is a descriptive analysis of survey data that identifies patterns and prevalence but does not evaluate any intervention to address child labour or exploitation. The document does not provide details on study design, sampling methodology, or causal mechanisms of exploitation.
Abstract
The new Domestic Workers Dataset is the largest single set of surveys (n = 11,759) of domestic workers to date. Our analysis of this dataset reveals features about the lives and work of this “hard-to-find” population in India—a country estimated to have the largest number of people living in forms of contemporary slavery (11 million). The data allow us to identify child labour, indicators of forced labour, and patterns of exploitation—including labour paid below the minimum wage—using bivariate analysis, factor analysis, and spatial analysis. The dataset also helps to advance our understanding of how to measure labour exploitation and modern slavery by showing the value of “found data” and participatory and citizen science approaches.