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Estudio de causalidad sobre problemas de inclusión financiera con técnicas de ciencia de datos: el caso de México

Author

Listed:
  • Itzel Coquis Rioja

    (Tecnologico de Monterrey (ITESM) Campus Mexico City)

  • Mario Iván Contreras Valdez

    (Tecnologico de Monterrey (ITESM) Campus Mexico City)

Abstract

El presente artículo explora las causas de la inclusión financiera entre la población mexicana. Con datos de la Encuesta Nacional de Inclusión Financiera (ENIF) (INEGI, 2021) desarrolla dos modelos de aprendizaje automático con el objetivo de identificar a individuos que forman parte del sis-tema financiero. Estos modelos son evaluados valiéndose tanto de metodologías de inteligencia artificial como de pruebas estadísticas de significancia tradicionales. Los hallazgos sugieren que factores como nivel educativo, ingreso mensual, preferencias orientadas hacia el futuro sobre las presentes, capacidad de ahorro y acceso a teléfonos inteligentes son impulsores significativos que aumentan la probabilidad de inclusión financiera. En consecuencia, existe un potencial para la implementación de políticas públicas dirigidas a incentivar a los individuos para que adopten voluntariamente servicios financieros formales.

Suggested Citation

  • Itzel Coquis Rioja & Mario Iván Contreras Valdez, 2024. "Estudio de causalidad sobre problemas de inclusión financiera con técnicas de ciencia de datos: el caso de México," The Anahuac Journal, Business and Economics School. Anahuac University (Mexico)., vol. 24(1), pages 246-271, June.
    • Handle: RePEc:amj:journl:v:24:y:2024:i:1:p:246-271
    DOI: https://doi.org/10.36105/theanahuacjour.2024v24n1.09
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    References listed on IDEAS

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    1. John Conroy, 2005. "APEC and financial exclusion: missed opportunities for collective action?," Asia-Pacific Development Journal, United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), vol. 12(1), pages 53-79, June.
    2. Md Jamil, Abd Rahim & Law, Siong Hook & Khair-Afham, M.S. & Trinugroho, Irwan, 2024. "Financial inclusion and income inequality in developing countries: The role of aging populations," Research in International Business and Finance, Elsevier, vol. 67(PA).
    3. Mandira Sarma, 2015. "Measuring financial inclusion," Economics Bulletin, AccessEcon, vol. 35(1), pages 604-611.
    4. Schuetz, Sebastian & Venkatesh, Viswanath, 2020. "Blockchain, adoption, and financial inclusion in India: Research opportunities," International Journal of Information Management, Elsevier, vol. 52(C).
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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

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