During the program, there were several vacancies registered in the program that were not filled and several young people enrolled in the program were not hired. For this reason, this study had the objective of studying the gaps between labor demand and supply in the context of the program. This work was carried out within the framework of an impact evaluation project of the program, conducted jointly with Econometría Consultores and SEI Consultores.
Using text mining and natural language processing, the gaps between labor supply and demand in the context of the program were studied. In particular, the Latent Dirichlet Allocation (LDA) topic model was used. Two text samples were available: job descriptions and resumes. The topic model was trained on the resumes and then applied to the job descriptions to compare the texts representing labor demand and supply in the same vector space. Subsequently, through the composition of the topics and the Euclidean distance between the texts, the gaps between what the young people offered and what the companies demanded from them were identified by qualitatively analyzing each topic.
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