BRECHA DIGITAL Y EXCLUSIÓN FINANCIERA DIGITAL EN PERSONAS ADULTAS MAYORES DEL PERÚ, 2024
DOI:
https://doi.org/10.33324/udaakadem.vi18.1222Parole chiave:
alfabetización digital, desigualdad digital, envejecimiento, inclusión financiera, uso de internetAbstract
El presente estudio analizó la relación entre dimensiones de la brecha digital y la exclusión financiera digital (EFD) en adultos mayores durante 2024. Para ello, se utilizaron los microdatos de la Encuesta Nacional de Hogares (ENAHO) 2024 y una regresión logística ajustada al diseño muestral complejo. La EFD se operacionalizó como una proxy acotada, basada en el no uso de medios digitales para comprar alimentos de pan llevar. El análisis adoptó una perspectiva multidimensional de la desigualdad digital y utilizó la justicia digital como marco interpretativo. El modelo incluyó 19 167 observaciones. La prevalencia ponderada de EFD fue 96.10 %. El IHD e IUETIC se asociaron con reducciones de 6.77 y 11.32 puntos porcentuales, respectivamente; la edad se asoció con un incremento de 0.24 puntos porcentuales por año. Educación y territorio mostraron asociaciones significativas. IRD, Internet del hogar, sexo y celular no mostraron asociaciones independientes significativas. Se concluyó que la EFD estuvo asociada con capacidades, usos digitales y heterogeneidades sociodemográficas y territoriales.
Palabras clave: alfabetización digital, desigualdad digital, envejecimiento, inclusión financiera, uso de internet
Abstract
This study analyzed the relationship between dimensions of the digital divide and digital financial exclusion (DFE) among older adults in Peru in 2024. Microdata from ENAHO 2024 and logistic regression adjusted for the complex survey design were used. DFE was operationalized as a bounded proxy based on not using digital means to purchase staple foods. The analysis adopted a multidimensional perspective of digital inequality and used digital justice as an interpretive framework. The model included 19,167 observations. Weighted DFE prevalence was 96.10%. IHD and IUETIC were associated with reductions of 6.77 and 11.32 percentage points, respectively; age was associated with a 0.24 percentage-point increase per year. Education and territory also showed significant associations. Corrected IRD, household Internet access, sex, and mobile phone availability showed no significant independent associations. It was concluded that DFE was associated with digital capabilities, digital use, and sociodemographic and territorial heterogeneity at the overall population level.
Keywords: aging, digital inequality, digital literacy, financial inclusion, internet use
Downloads
Riferimenti bibliografici
Archer, K., & Lemeshow, S. (2006). Goodness-of-fit test for a logistic regression model fitted using survey sample data. The Stata Journal, 6(1), 97-105. https://doi.org/10.1177/1536867X0600600106
Aurazo, J., & Vega, M. (2021). Why people use digital payments: Evidence from micro data in Peru. Latin American Journal of Central Banking, 2(4), 100044. https://doi.org/10.1016/j.latcb.2021.100044
Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3. https://doi.org/10.1175/1520-0493(1950)078<0001: VOFEIT>2.0.CO;2
Cameron, A., & Trivedi, P. (2005). Microeconometrics: Methods and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511811241
Creswell, J., & Creswell, J. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Escuder, S., Liesegang, R., & Rivoir, A. (2020). Usos y competencias digitales en personas mayores beneficiarias de un plan de inclusión digital en Uruguay. Psicología, Conocimiento y Sociedad, 10(1), 54-80. https://doi.org/10.26864/PCS.v10.n1.3
Fox, J., & Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417), 178-183. https://doi.org/10.1080/01621459.1992.10475190
Friemel, T. (2016). The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society, 18(2), 313-331. https://doi.org/10.1177/1461444814538648
Hargittai, E., & Dobransky, K. (2017). Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication, 42(2), 195-212. https://doi.org/10.22230/cjc.2017v42n2a3176
Heeks, R. (2021). From digital divide to digital justice in the Global South: Conceptualising adverse digital incorporation (Digital Development Working Paper No. 90). Centre for Digital Development, University of Manchester. https://doi.org/10.2139/ssrn.3907633
Helsper, E. (2012). A corresponding fields model for the links between social and digital exclusion. Communication Theory, 22(4), 403-426. https://doi.org/10.1111/j.1468-2885.2012.01416.x
Helsper, E. (2021). The digital disconnect: The social causes and consequences of digital inequalities. SAGE Publications. https://doi.org/10.4135/9781526492982
Helsper, E., & Eynon, R. (2013). Distinct skill pathways to digital engagement. European Journal of Communication, 28(6), 696-713. https://doi.org/10.1177/0267323113499113
Hosmer, D., Jr., Lemeshow, S., & Sturdivant, R. (2013). Applied logistic regression (3rd ed.). Wiley. https://doi.org/10.1002/9781118548387
Instituto Nacional de Estadística e Informática. (s.f.). Sistema de Microdatos. Recuperado el 2 de septiembre de 2026.
https://proyectos.inei.gob.pe/microdatos/
Instituto Nacional de Estadística e Informática. (2024a). Encuesta Nacional de Hogares 2024: Diccionario de datos.
Instituto Nacional de Estadística e Informática. (2024b). Encuesta Nacional de Hogares 2024: Ficha técnica.
Instituto Nacional de Estadística e Informática. (2025). Las tecnologías de información y comunicación en los hogares: II trimestre 2025. https://www1.inei.gob.pe/media/MenuRecursivo/boletines/informetecnico_tics_iit25.pdf
Klapper, L., Singer, D., Starita, L., & Norris, A. (2025). The Global Findex Database 2025: Connectivity and financial inclusion in the digital economy. World Bank. https://doi.org/10.1596/978-1-4648-2204-9
Little, R., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Wiley.
https://doi.org/10.1002/9781119482260
Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(8), 1-19. https://doi.org/10.18637/jss.v009.i08
Lumley, T. (2010). Complex surveys: A guide to analysis using R. Wiley.
https://doi.org/10.1002/9780470580066
Lumley, T. (2017). Pseudo-R² statistics under complex sampling. Australian & New Zealand Journal of Statistics, 59(2), 187-194. https://doi.org/10.1111/anzs.12187
Lumley, T., & Scott, A. (2015). AIC and BIC for modeling with complex survey data. Journal of Survey Statistics and Methodology, 3(1), 1-18. https://doi.org/10.1093/jssam/smu021
Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63-73. https://doi.org/10.1016/j.jclinepi.2019.02.016
Martínez-Domínguez, M., & Mora-Rivera, J. (2020). Internet adoption and usage patterns in rural Mexico. Technology in Society, 60, 101226. https://doi.org/10.1016/j.techsoc.2019.101226
Mei, Y. (2024). Exploring the mechanisms driving elderly Fintech engagement: The role of social influence and the elderly’s digital literacy. Frontiers in Psychology, 15, 1420147. https://doi.org/10.3389/fpsyg.2024.1420147
Ministerio de Economía y Finanzas. (2021). Plan Estratégico Multisectorial de la Política Nacional de Inclusión Financiera. https://www.mef.gob.pe/contenidos/inclusion_financiera/documentos/PNIF_plan_estrategico_mutisectorial.pdf
Neumann, P., Araki, S., & Gutterman, E. (2000). The use of proxy respondents in studies of older adults: Lessons, challenges, and opportunities. Journal of the American Geriatrics Society, 48(12), 1646-1654.
https://doi.org/10.1111/j.1532-5415.2000.tb03877.x
Presidencia del Consejo de Ministros. (2001). Decreto Supremo N.º 043-2001-PCM: Reglamento de Organización y Funciones del Instituto Nacional de Estadística e Informática. https://www.gob.pe/institucion/inei/informes-publicaciones/2394038-reglamento-de-organizacion-y-funciones-rof
R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://doi.org/10.32614/R.manuals
Scheerder, A., van Deursen, A., & van Dijk, J. (2017). Determinants of Internet skills, uses and outcomes: A systematic review of the second- and third-level digital divide. Telematics and Informatics, 34(8), 1607-1624. https://doi.org/10.1016/j.tele.2017.07.007
Steyerberg, E., Vickers, A., Cook, N., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M., & Kattan, M. (2010). Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology, 21(1), 128-138. https://doi.org/10.1097/EDE.0b013e3181c30fb2
Sunkel, G., & Ullmann, H. (2019). Las personas mayores de América Latina en la era digital: Superación de la brecha digital. Revista de la CEPAL, 127, 243-268. https://doi.org/10.18356/db143bd3-es
Tirado-Morueta, R., Rodríguez-Martín, A., Álvarez-Arregui, E., Ortíz-Sobrino, M. Á., & Aguaded-Gómez, J. I. (2021). Understanding internet appropriation among older people through institutional supports in Spain. Technology in Society, 64, 101505. https://doi.org/10.1016/j.techsoc.2020.101505
van Deursen, A., & Helsper, E. (2015). The third-level digital divide: Who benefits most from being online? In L. Robinson, S. R. Cotten, J. Schulz, T. M. Hale, & A. Williams (Eds.), Communication and information technologies annual: Digital distinctions and inequalities (Vol. 10, pp. 29-52). Emerald Group Publishing. https://doi.org/10.1108/S2050-206020150000010002
van Deursen, A., & van Dijk, J. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507-526. https://doi.org/10.1177/1461444813487959
van Dijk, J. (2020). The digital divide. Polity Press.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1, Suppl.), S65-S71.
https://doi.org/10.1016/j.chest.2020.03.012
Williams, R. (2012). Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal, 12(2), 308-331. https://doi.org/10.1177/1536867X1201200209
Dowloads
Pubblicato
Fascicolo
Sezione
Licenza
Copyright (c) 2026 UDAAKADEM
Questo volume è pubblicato con la licenza Creative Commons Attribuzione - Non commerciale - Condividi allo stesso modo 4.0 Internazionale.
Google Académico