Exploring spatial non-stationarity of child labour and its related factors: A multiscale geographically weighted regression study of India

The relationships between various factors and child labour have been explored in the literature but, despite findings that suggest the predictive factors of child labour can vary according to context, there has been little research that has used spatial methods of analysis or attempted to estimate local relationships between covariates and the prevalence of child labour. This paper seeks to address this knowledge gap by using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models. Using India 2011 Census Data as a case study, the findings show that GWR and MGWR models both perform significantly better than a global regression model across the whole of India. The study also finds significant spatial non-stationarity in the relationships between child labour and its covariates, with the association between district-level child labour rates and both the Muslim population and the child sex ratio found to have opposite directions in different parts of the country. Using MGWR, it was also possible to demonstrate that different covariates interact with child labour at various spatial scales, suggesting that interventions aiming to address varying aspects of the child labour problem may need to be deployed at different administrative levels to maximise their efficacy. • GWR and MGWR models of child labour in India outperform a global model. • Various factors have different associations with child labour based on location. • The spatial scales of the relationships with child labour vary by factor. • Both location and scale should be considered in child labour interventions.