Examinando por Materia "Mercado laboral colombiano"
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Publicación Análisis relacional entre la formación y las ocupaciones laborales de los egresados de educación para el Trabajo y el Desarrollo Humano (ETDH) en Colombia entre 2021 y 2023(Universidad EAFIT, 2024) Sánchez Calderín, Luis Felipe; Chaparro Cardona, Juan CamiloThis study focuses on the relationship between labor occupations and the educational training of graduates of Education for Work and Human Development (ETDH) programs between 2021 and 2023. Based on the information provided by DANE through the Gran Encuesta Integrada de Hogares (GEIH), the correspondence (fit) and mismatch (mismatch) between studies and occupations is calculated, with a focus on gender and regional gaps. Additionally, a logit model is used to estimate the influence of certain factors on this relationship, such as age, gender, region of origin and the economic sector in which the graduate is employed. The results suggest that there are differences between the regions of Colombia in terms of the match between training and occupations. The gaps stand out in the Orinoquía, Llanos and Amazonía region, where the mismatch is more pronounced. In turn, women face complex conditions, as they have a higher proportion of mismatches compared to men.Ítem The Effect of Increased Schooling in the Colombian Labor Market Between 2008 and 2016(Universidad EAFIT, 2017-06-22) Aristizábal Lopera, Tomás; Ángel López, Esteban; Universidad EAFITPublicación Inteligencia del mercado laboral colombiano : detección automatizada de habilidades mediante modelos grandes de lenguaje (LLM) y recuperación aumentada (RAG)(Universidad EAFIT, 2025) Zapata Posada, Jorge Mario; Álvarez Barrera, Claudia Patricia; Padilla Buritica, Jorge IvánThe demand for skills in the labor market has evolved significantly in recent decades, driven by changes in the economic environment and constant technological advances. In this context, the detailed description of each job offer, available on employment web portals, provides accurate information on the specific skills required by the market in real time. Labor Market Intelligence (LMI) research uses this data along with machine learning algorithms to anticipate trends and understand the evolution of talent demand. Despite advances in artificial intelligence and the availability of large data volumes, there remains a gap in adapting these technologies to local contexts. Regional markets, such as Colombia, require customized approaches to ensure that technological solutions respond to the specific needs of the labor market, effectively aligning talent supply and demand. This study analyzes data from the Talent.com employment platform for Colombia using a state-of-the-art approach based on Large Language Models (LLM) combined with Retrieval Augmented Generation (RAG) to identify emerging, traditional, technical, and soft skills. In the first stage, a multilingual LLM extracts skill mentions from job descriptions. In the second stage, a semantic retrieval module queries the European Commission’s open ESCO skills taxonomy to propose standardized candidate labels, the LLM then selects the most appropriate label and delivers validated, structured JSON outputs. Preliminary results show improvements in precision, coverage, and auditability compared to purely supervised approaches, reducing hallucinations through candidate-constrained selection and standardizing categories using ESCO skill classification. This framework provides valuable insights that, in future work, may support universities in designing academic programs aligned with labor market needs, thus facilitating strategic decision-making for employers, policymakers, and educators, and contributing to talent development and the reduction of unemployment in Colombia.Publicación Sistema multi-agente para la preselección de candidatos en vacantes públicas de empleo utilizando inteligencia artificial generativa(Universidad EAFIT, 2025) Blandón Londoño, Cristian Mauricio; Álvarez Barrera, Claudia Patricia; Martínez Vargas, Juan DavidHistorically, recruitment processes have been carried out manually. In such processes, candidates go through a series of filters that vary depending on the specific requirements of each vacancy. As these procedures are not standardized, their duration can be extended, leading to an increase in unfilled positions and, consequently, negatively impacting organizational competitiveness. Identifying the ideal candidate for a job vacancy is a task that demands both time and resources. Today, this represents a significant challenge for organizations within the Human Resources sector, where each day spent searching for the right talent translates into operational costs. As a result, delays in recruitment activities directly affect the achievement of strategic organizational goals. Despite the growing adoption of Applicant Tracking Systems (ATS), these tools often face semantic limitations and do not easily adapt to local contexts—especially in countries like Colombia, where a significant portion of the population is employed informally. This reality hinders not only the objective assessment of candidate suitability but also increases the likelihood of evaluative biases. Recent studies have begun exploring the integration of multi-agent architectures with Large Language Models (LLMs) to automate pre-screening processes. In line with this, the present project proposes the implementation of a multi-agent system for candidate evaluation. By combining Natural Language Processing (NLP) techniques with LLMs, the system aims to analyze applicant and job posting data to support human resources professionals in determining candidate-job fit. The system will be designed to optimize evaluation procedures in recruitment, with the goal of reducing the average time required for candidate assessment and selection. Furthermore, implementation seeks to minimize manual operations and mitigate bias in the evaluation process, thereby contributing to the sustainable development of human capital. It is anticipated that this solution will increase the efficiency of recruitment workflows and promote greater alignment between the skills demanded by employers and those offered in the labor market. This, in turn, is expected to benefit both employers and job seekers, and indirectly support efforts to bridge the skills gap in the Colombian labor market—particularly in a context characterized by high levels of informality—by establishing fair and competency-based evaluation criteria.