Child Labor, Informality, and Poverty: Leveraging Logistic Regression, Indeterminate Likert Scales, and Similarity Measures for Insightful Analysis in Ecuador

Abstract

The paper presents a comprehensive analysis of child labor in Ecuador, employing advanced statistical tools like logistic regression, neutrosophic Likert scales, and similarity measures to deepen the understanding of this social issue. The integration of these methodologies allows for a nuanced assessment of the various socio-economic factors contributing to child labor. By capturing the uncertainty in human responses, the research highlights the complex interplay between poverty, household income, education levels, and labor types on the incidence of child labor.Key findings suggest that rural location, the age of the child, and the informal nature of the head of the household'swork are the most significant predictors of child labor. Notably, parental education appears to have a less direct influence. Despite various efforts, including government monetary transfers through programs like the BDH, child labor persists, indicating the need for more targeted interventions.The paper proposes future research to extend these models to a broader demographic and geographic data set, emphasizing the potential for these methods to be applied to a variety of social issues. The development ofcomputational tools to automate neutrosophic analysis could greatly benefit large-scale studies, potentially aiding policymakers in designing more effective interventions for vulnerable populations.

Keywords

• child labor
• logistic regression
• neutrosophic scales
• indeterminacy