An algorithm to estimate the risk of child labor

• Autores: Quiñones Fabian, Ricky Bryan, Inga Lopez, Abel Jesus, Andamayo Alcantara, Ruben Aldair, Deza Quispe, Jimmy Alberth, Huaytalla Pariona, Jaime Antonio
• Formato: tesis de grado
• Fecha de Publicación: 2022
• Institución: Universidad Continental
• Repositorio: CONTINENTAL-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.continental.edu.pe:20.500.12394/12340
• Enlace del recurso: https://hdl.handle.net/20.500.12394/12340; https://doi.org/10.5267/j.dsl.2022.5.004
• Nivel de acceso: acceso abierto
• Materia: Trabajo infantil; Pobreza; Clases sociales; https://purl.org/pe-repo/ocde/ford#2.02.02

In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.