Microeconomic Determinants of Youth Unemployment in Ecuador: An analysis with data from the ENEMDU 2024 fourth quarter
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Abstract
This study examines the microeconomic determinants of youth unemployment in Ecuador using representative data from ENEMDU 2024-IV. The objective is to model the unemployment probability for individuals aged 18 to 29 through weighted logistic regression incorporating complex survey design (clusters, strata, and expansion factors).
Key sociodemographic variables (residence area, gender, age, household head status, education level, ethnic minority status, and marital status) were selected, and assumptions of multicollinearity, logit linearity, and goodness of fit were validated. The findings provide a robust methodological framework and recommendations for public policy aimed at enhancing youth labor market integration.
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References
Bustos, R., & Campozano, M. (2017). Factores asociados al desempleo juvenil en Ecuador: Un enfoque econométrico [Factors associated with youth unemployment in Ecuador: An econometric approach]. Revista Ecuatoriana de Estadística, 15(2), 45-60.
Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215–232.
Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression. Wiley.
Instituto Nacional de Estadística y Censos (INEC). (2024). Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU) – IV Trimestre 2024 [Microdata].
International Labour Organization (ILO). (2023). Global employment trends for youth 2023.
Libaque-Saenz, C. F., et al. (2017). Neural networks vs. logistic regression in social phenomena prediction: The case of child labor in Peru. Latin American Journal of Economics, 54(1), 89–112.
Linthon Delgado, J., et al. (2024). Determinantes del desempleo juvenil en Ecuador: Un análisis comparativo [Determinants of youth unemployment in Ecuador: A comparative analysis]. FLACSO Ecuador.
Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(1), 1–19.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Chapman & Hall.
Tulcanaza-Prieto, A. B., et al. (2023). Youth unemployment in Ecuador: Analysis with ENEMDU 2019. Journal of Latin American Studies, 51(3), 345–367.
He, H., & García, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.
Sánchez, J., & López, M. (2020). Desempleo juvenil y sobrecualificación en Ecuador: un análisis con datos de la ENEMDU. Revista de Economía del Desarrollo, 25(2), 45-62.
Blanchard, O. J., & Katz, L. F. (1999). Wage Dynamics: Reconciling Theory and Evidence.
Comisión Económica para América Latina y el Caribe (CEPAL). (2021). Panorama Social de América Latina 2021. Naciones Unidas.
Dspace UNL. (2022). Análisis del desempleo juvenil en América Latina. Universidad Nacional de Loja.
Pérez, A., González, C., & Rodríguez, D. (2023). Predictores del desempleo juvenil en Barranquilla: Un enfoque de machine learning. Revista Colombiana de Economía, 15(2), 112-130.
Primicias. (2025). El 36% de los desempleados en Ecuador nunca ha tenido un trabajo.
Radiopichincha. (2025). Tasa de desempleo juvenil en Ecuador: una problemática crítica.
Religación. (2024). Factores microeconómicos del desempleo juvenil en Ecuador. Revista Religación, 9(45), 1-15.
Santoso, B., & Wijaya, D. (2024). Addressing Imbalanced Data in Predicting Unemployment Rates: A Random Forest and SMOTE Approach in Maluku Province. Indonesian Journal of Statistics, 10(1), 45-60.