An algorithm to estimate the risk of child labor

Ricky Bryan Quiñones Fabian; Ruben Aldair Andamayo Alcantara; Abel Jesus Inga Lopez; Jaime Antonio Huaytalla Pariona; Jimmy Alberth Deza Quispe

doi:10.5267/j.dsl.2022.5.004

An algorithm to estimate the risk of child labor

Ricky Bryan Quiñones Fabian, Ruben Aldair Andamayo Alcantara, Abel Jesus Inga Lopez, Jaime Antonio Huaytalla Pariona, Jimmy Alberth Deza Quispe

School of Mechatronics Engineering

Research output: Contribution to journal › Original Article › peer-review

Abstract

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.

Original language	American English
Pages (from-to)	521-528
Number of pages	8
Journal	Decision Science Letters
Volume	11
Issue number	4
DOIs	https://doi.org/10.5267/j.dsl.2022.5.004
State	Indexed - 1 Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors; licensee Growing Science, Canada. © 2022 by the authors; distributed under the term.

Keywords

Child labor
Lasso
Logit
Poverty
Social stratification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.5267/j.dsl.2022.5.004

Cite this

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title = "An algorithm to estimate the risk of child labor",

abstract = "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.",

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AU - Fabian, Ricky Bryan Quiñones

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AU - Pariona, Jaime Antonio Huaytalla

AU - Quispe, Jimmy Alberth Deza

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N2 - 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.

AB - 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.

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KW - Logit

KW - Poverty

KW - Social stratification

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An algorithm to estimate the risk of child labor

Abstract

Bibliographical note

Keywords

UN SDGs

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