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Could Machine Learning Improve the Prediction of Child Labor in Peru?

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 795))

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Abstract

Child labor is a relevant problem in developing countries because it may have a negative impact on economic growth. Policy makers and government agencies need information to correctly allocate their scarce resources to deal with this problem. Although there is research attempting to predict the causes of child labor, previous studies have used only linear statistical models. Non-linear models may improve predictive capacity and thus optimize resource allocation. However, the use of these techniques in this field remains unexplored. Using data from Peru, our study compares the predictive capability of the traditional logit model with artificial neural networks. Our results show that neural networks could provide better predictions than the logit model. Findings suggest that geographical indicators, income levels, gender, family composition and educational levels significantly predict child labor. Moreover, the neural network suggests the relevance of each factor which could be useful to prioritize strategies. As a whole, the neural network could help government agencies to tailor their strategies and allocate resources more efficiently.

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Author information

Authors and Affiliations

  1. Universidad del Pacífico, Avenida Salaverry 2020, Jesús María, Lima 11, Peru

    Christian Fernando Libaque-Saenz, Juan Lazo & Edgardo R. Bravo

  2. Pontificia Universidad Católica del Perú, Av. Universitaria 1801, San Miguel, Lima 32, Peru

    Karla Gabriela Lopez-Yucra

Authors
  1. Christian Fernando Libaque-Saenz
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  2. Juan Lazo
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  3. Karla Gabriela Lopez-Yucra
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  4. Edgardo R. Bravo
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Corresponding author

Correspondence to Christian Fernando Libaque-Saenz .

Editor information

Editors and Affiliations

  1. University of Florida, Gainesville, Florida, USA

    Juan Antonio Lossio-Ventura

  2. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Libaque-Saenz, C.F., Lazo, J., Lopez-Yucra, K.G., Bravo, E.R. (2018). Could Machine Learning Improve the Prediction of Child Labor in Peru?. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2017. Communications in Computer and Information Science, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-90596-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-90596-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90595-2

  • Online ISBN: 978-3-319-90596-9

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