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

EAP Ingeniería mecatrónica

Producción científica: Artículo Científico › Artículo original › revisión exhaustiva

Resumen

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.

Idioma original	Inglés estadounidense
Páginas (desde-hasta)	521-528
-	8
Publicación	Decision Science Letters
Volumen	11
N.º	4
DOI	https://doi.org/10.5267/j.dsl.2022.5.004
Estado	Indizado - 1 set. 2022

Nota bibliográfica

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

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 1: Fin de la pobreza
ODS 4: Educación de calidad
ODS 8: Trabajo decente y crecimiento económico

Acceder al documento

10.5267/j.dsl.2022.5.004

Citar esto

@article{e0317deaf12c48a4964cdfd1d99546d1,

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.",

keywords = "Child labor, Lasso, Logit, Poverty, Social stratification",

author = "Fabian, \{Ricky Bryan Qui{\~n}ones\} and Alcantara, \{Ruben Aldair Andamayo\} and Lopez, \{Abel Jesus Inga\} and Pariona, \{Jaime Antonio Huaytalla\} and Quispe, \{Jimmy Alberth Deza\}",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors; licensee Growing Science, Canada. {\textcopyright} 2022 by the authors; distributed under the term.",

year = "2022",

month = sep,

day = "1",

doi = "10.5267/j.dsl.2022.5.004",

language = "American English",

volume = "11",

pages = "521--528",

journal = "Decision Science Letters",

issn = "1929-5804",

publisher = "Growing Science",

number = "4",

}

TY - JOUR

T1 - An algorithm to estimate the risk of child labor

AU - Fabian, Ricky Bryan Quiñones

AU - Alcantara, Ruben Aldair Andamayo

AU - Lopez, Abel Jesus Inga

AU - Pariona, Jaime Antonio Huaytalla

AU - Quispe, Jimmy Alberth Deza

PY - 2022/9/1

Y1 - 2022/9/1

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.

KW - Child labor

KW - Lasso

KW - Logit

KW - Poverty

KW - Social stratification

UR - https://www.scopus.com/pages/publications/85137764507

U2 - 10.5267/j.dsl.2022.5.004

DO - 10.5267/j.dsl.2022.5.004

M3 - Original Article

AN - SCOPUS:85137764507

SN - 1929-5804

VL - 11

SP - 521

EP - 528

JO - Decision Science Letters

JF - Decision Science Letters

IS - 4

ER -

An algorithm to estimate the risk of child labor

Resumen

Nota bibliográfica

ODS de las Naciones Unidas

Acceder al documento

Huella

Citar esto