Could machine learning improve the prediction of child labor in Peru?

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.