Examining Spatial Interdependencies in Child Labor and Socioeconomic Factors in Punjab, Pakistan

Embarking on a transformative journey to eradicate child labor, securing a brighter, unburdened future for every child-because every child deserves a childhood, not a workplace. This study aims to address the pervasive issue of child labor, by examining the interplay between socioeconomic factors and spatial patterns, ultimately striving to secure a brighter and unburdened future for children. It examines several factors contributing to child labor in Punjab, Pakistan, using a strong theoretical framework that stresses how important it is for parents to get more education and make their families' incomes rise. Utilizing data sourced from the Multiple Indicator Cluster Surveys (MICS) conducted by the Bureau of Statistics Punjab and UNICEF, this study examines child labor across 36 districts of Punjab through the application of logistic regression and multilevel logistic regression models. The results show that rising household income and parental education play an important part in reducing child labor. Factors such as the size of the household, the age of the children, the availability of educational and health institutions, institutional factors, and poverty play a role in influencing child labor. The results of the univariate and bivariate Moran's I spatial analysis reveal distinct clusters, emphasizing the urgency of implementing targeted interventions in each district based on the identified spatial patterns. This study contributes scientific value by analyzing district-level variations in child labor through multilevel and spatial analysis. Policy recommendations emphasize the implementation of targeted strategies aligned with specific Sustainable Development Goals, including 8.7 and 16.2, aiming to secure a promising future for children through measures targeting household income, educational infrastructure, age-appropriate interventions, law enforcement, and healthcare investment.