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

Research output: Contribution to journalOriginal Articlepeer-review


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 languageAmerican English
Pages (from-to)521-528
Number of pages8
JournalDecision Science Letters
Issue number4
StateIndexed - 1 Sep 2022

Bibliographical note

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


  • Child labor
  • Lasso
  • Logit
  • Poverty
  • Social stratification


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