Desigualdades de bienestar en Europa en función del nivel educativo: un análisis transnacional y multidimensional con machine learning

• Alejandra Ramos ^[1] ; María Teresa Ballestar ^[1] ; Kathrin Komp-Leukkunen ^[2] ; Jorge Sainz ^[1]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] University of Technology

     University of Technology

     Rusia

     
• Localización: Papeles de economía española, ISSN 0210-9107, Nº 184, 2025 (Ejemplar dedicado a: La inteligencia artificial en el sistema educativo), págs. 189-217
• Idioma: español
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Esta investigación analiza cómo el nivel educativo influye en múltiples dimensiones del bienestar individual en Europa, utilizando la Ronda 11 (2023/2024) de la Encuesta social europea para España, Finlandia y Grecia. Mediante una Red Bayesiana Ingenua Aumentada de Árbol (TAN), se modelizan las relaciones entre la educación y variables como ingresos, salud, empleo, comportamiento digital, felicidad y la interacción social. El modelo alcanza una precisión robusta del 71,09 por 100 y revela que la educación superior se asocia con mejores resultados económicos, mayor bienestar subjetivo, mejor salud y mayor compromiso digital. También contribuye a reducir diferencias de género en el empleo y mitigar efectos del envejecimiento y deterioro de la salud, favoreciendo la actividad laboral y conexión digital en edades avanzadas. Sin embargo, la relación con la interacción social depende del contexto. Los hallazgos destacan el papel de la educación como motor de inclusión y resiliencia en múltiples ámbitos de la vida.

  • English

    This study examines how educational attainment influences multiple dimensions of individual wellbeing across Europe, using data from Round 11 (2023/2024) of the European Social Survey for Spain, Finland, and Greece. An Augmented tree-Augmented naive Bayesian network (TAN) is applied to model the relationships between education and variables such as income, health, employment, digital engagement, happiness, and social interaction. The model achieves a robust classification accuracy of 71.09% and shows that higher education is systematically associated with better economic outcomes, improved health, greater digital engagement, and higher subjective wellbeing. It also contributes to reducing gender disparities in paid employment and mitigating the effects of ageing and health deterioration, supporting continued participation in the labour market and digital connectivity at older ages. However, the relationship between education and social interaction appears to be context-dependent. These findings underscore the multidimensional role of education as a driver of inclusion and resilience across life domains.

• Referencias bibliográficas
  • Aalders, I. (2008). Modeling land-use decision behavior with Bayesian belief networks. Ecology and Society, 13 (1).
  • Agasisti, T., Avvisati, F., Borgonovi, F. y Longobardi, S. (2021).What School Factors are Associated with the Success of Socio-Economically...
  • Akerlof, G. A. y Kranton, R. E. (2002). Identity and schooling: Some lessons for the economics of education. Journal of Economic Literature,...
  • Araki, S. (2022). Does Education Make People Happy? Spotlighting the Overlooked Societal Condition. Journal of Happiness Studies, 23, 2. Netherlands:...
  • Bailey, R. (2009). Well-being, happiness and education. British Journal of Sociology of Education, 30(6), 795—802. https://doi.org/10.1080/01425690903236613
  • Ballestar, M. T., Freire-Rubio, M. T. y Ortigosa-Blanch, A. (2023). Tutoring and Its Effects on Academic Achievement: A Policy Evaluation...
  • Ballestar, M. T., Garcia-Lázaro, A., Sainz, J., Sanz, I., García-Lázaro, A., Sainz, J. y Sanz, I. (2022). Why is your company not robotic?...
  • Ballestar, M. T. M. T., Sainz, J. y Sanz, I. (2022). An economic evaluation of educational interventions in the LOMLOE: Proposals for improvement...
  • Becker, G. S. (1964). Human capital. National Bureau of Economic Research.Bielza, C. y Larrañaga, P. (2014).Bayesian networks in neuroscience:...
  • Borgonovi, F. y Pál, J. (2016). A framework for the analysis of student well-being in the PISA 2015 study: Being 15 in 2015.
  • Borgonovi, F. y Pokropek, A. (2025). Young people’s social mobility expectations in an unequal world. Science. https://doi.org//10.1126/science.adt1842...
  • Brown, C. y Shay, M. (2021). From resilience to wellbeing: Identity-building as an alternative framework for schools’ role in promoting children’s...
  • Buttler, D. (2022). Employment Status and Well-Being Among Young Individuals. Why Do We Observe Cross-Countr y Differences? Social Indicators...
  • Chen, S. H. y Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling y Software, 37, 134—145 .
  • Chen, X.-W., Anantha, G. y Lin, X. (2008). Improving Bayesian network structure learning with mutual information-based node ordering in the...
  • Cheon, S.-P., Kim, S., Lee, S.-Y. y Lee, C.-B. (2009). Bayesian networks based rare event prediction with sensor data. Knowledge-Based Systems,...
  • Coenen, J., Borghans, L. y Diris, R. (2021). Personality traits, preferences and educational choices: A focus on STEM. Journal of Economic...
  • Dželihodžić, A., Đonko, D. y Jonko, D. (2016). Comparison of ensemble classification techniques and single classifiers performance for customer...
  • Elsalamony, H. A. (2014). Bank direct marketing analysis of data mining techniques. International Journal of Computer Applications, 85(7).
  • ESS ERIC (2025). European Social Survey. https://www.europeansocialsurvey.org/about-ess
  • Farmani, R., Henriksen, H. J., Savic, D. y Butler, D. (2012).An evolutionary Bayesian belief network methodology for participator y decision...
  • Fenton, N. y Neil, M. (2014). Decision support software for probabilistic risk assessment using Bayesian networks. IEEE Software.
  • Firth, J. A., Hellewell, J., Klepac, P., Kissler, S., Kucharski, A. J. y Spurgin, L. G. (2020). Using a real-world network to model localized...
  • Fitzek, F. H. P., Granelli, F. y Seeling, P. (2020).Computing in communication networks: from theory to practice. Academic Press. https://worldhappiness.report/ed/2025/
  • Frey, B. S. y Stutzer, A. (2002). What can economists learn from happiness research? Journal of Economic Literature, 40(2), 402—435. https://doi.org/10.1257/jel.40.2.402
  • Hanea, D., Hanea, A., Ale, B., Sillem, S., Lin, P. H., Van Gulijk, C. y Hudson, P. (2012).Using dynamic Bayesian networks to implement feedback...
  • Harmon, C., Oosterbeek, H. y Walker, I. (2003). The returns to education: Microeconomics. Journal of Economic Surveys, 17(2), 115—156. https://doi.org/10.1111/1467-6419.00191
  • Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B. y Wang, S. (2025).World Happiness Report 2025. Wellbeing Research...
  • Hosmer Jr, D. W., Lemeshow, S. y Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley y Sons.
  • Jensen, H. L. (1992). Using neural networks for credit scoring. Managerial Finance.
  • Koski , T. y Noble, J. (2011). Bayesian networks: an introduction(Vol. 924). John Wiley y Sons.
  • Kristoffersen, I. (2018). Great expectations: Education and subjective wellbeing. Journal of Economic Psychology, 66(March), 64—78. https://doi.org/10.1016/j.joep.2018.04.005
  • Landuyt, D., Broekx, S., D’hondt, R., Engelen, G., Aertsens, J. y Goethals, P. L. M. (2013). A review of Bayesian belief networks in ecosystem...
  • Marcot, B. G. y Penman, T. D. (2019). Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling...
  • Marquez, J. y Main, G. (2021). Can Schools and Education Policy Make Children Happier? A Comparative Study in 33 Countries. Child Indicators...
  • Michalos, A. C. (2005).Multiple Discrepancies Theory (MDT). En A. C. Michalos (Ed.), Citation Classics from Social Indicators Research: The...
  • Michalos, A. C. (2008). Education, happiness and wellbeing. Social Indicators Research, 87(3), 347—366. https://doi.org/10.1007/s11205-007-9144-0
  • Neff, K. (2003). Self-compassion: An alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85—101.
  • OCDE (2024) . The PISA happy life dashboard: visualising key indicators on student well-being from the PISA survey. https://one.oecd.org/document/EDU/PISA/GB(2024)1/en/pdf
  • Powdthavee, N., Lekfuangfu, W. N. y Wooden, M. (2015).What’s the good of education on our overall quality of life? A simultaneous equation...
  • Schuck, B. y Steiber, N. (2018). Does Intergenerational Educational Mobility Shape the Well-Being of Young Europeans? Evidence from the European...
  • Seligman, M. E. P., Ernst, R. M., Gillham, J., Reivich, K. y Linkins, M. (2009) . Positive education: Positive psychology and classroom interventions....
  • Sen, A. (1999).Development as freedom. Oxford University Press.
  • Shastri, B. J., Tait, A. N., de Lima, T. F., Nahmias, M. A., Peng, H . -T. y Prucnal, P. R. (2017). Principles of neuromorphic photonics....
  • Shi, H. y Li, K. (2006). Comparison of Two Learning Methods of the Tree Augmented Naïve Bayesian Network Classifier 2006. International Conference...
  • Shi, H.-B. y Huang, H.-K. (2002). Learning tree-augmented naive Bayesian network by reduced space requirements. Proceedings. International...
  • Tan, J. J. X., Kraus, M. W., Carpenter, N. C. y Adler, N. E. (2020). The Association Between Objective and Subjective Socioeconomic Status...
  • Vanderweele, T. J. (2017).On the promotion of human flourishing. In Proceedings of the National Academy of Sciences of the United States of...
  • Yin, L., Ge, Y., Xiao, K., Wang, X. y Quan, X. (2013). Feature selection for high-dimensional imbalanced data. Neurocomputing, 105, 3—11.