Quantifying the Impact of Digital Access on Learning Outcomes: Evidence from Marginalized Regions in Sub-Saharan Africa
DOI:
https://doi.org/10.37870/joqie.v16i27.530Keywords:
digital access; learning outcomes; Digital Access Index; instrumental variables; difference-in-differences; Sub-Saharan Africa; educational technology; digital divide; causal inference; secondary educationAbstract
The digital divide between urban and rural populations in Sub-Saharan Africa (SSA) constitutes a growing threat to educational equity, yet rigorous causal evidence on the magnitude of digital access effects on student learning outcomes remains sparse. This study quantifies the causal impact of digital access on secondary school learning outcomes in marginalized regions across five SSA countries—Kenya, Tanzania, Uganda, Ghana, and Nigeria—over the period 2016 to 2022. A composite Digital Access Index (DAI) is constructed from International Telecommunication Union (ITU) indicators encompassing internet penetration, mobile broadband coverage, household device ownership, and electricity access, validated against World Bank household survey data. Two complementary causal identification strategies are employed. First, instrumental variable (IV) regression exploits plausibly exogenous variation in submarine cable landing proximity and national fibre optic backbone expansion timelines as instruments for regional digital access. Second, a difference-in-differences (DiD) design leverages staggered rollout of government-sponsored digital school connectivity programmes across districts to identify the within-district, pre-post effect of digital access on standardised examination scores. Learning outcomes are operationalised using standardised national examination scores in mathematics and English, drawn from national examination council microdata. The DAI exhibits a pooled mean of 0.381 (SD = 0.214) across the analytical sample, with a rural-urban gap of 0.312 index points. IV estimates indicate that a one standard deviation increase in DAI raises standardised examination scores by 0.241 SD (95% CI: [0.187, 0.295]; p < 0.001) after controlling for household wealth, parental education, teacher quality, school infrastructure, and district-year fixed effects. DiD estimates yield a consistent average treatment effect of 0.198 SD (95% CI: [0.141, 0.255]; p < 0.001). Effect sizes are larger for mathematics than English, larger for rural than urban districts, and larger for male than female students, revealing important heterogeneity that qualifies the aggregate findings. The study concludes with policy recommendations for targeted digital infrastructure investment, device provisioning, teacher digital literacy training, and gender-sensitive digital pedagogy
References
[1] Selwyn, N. (2016). Is technology good for education? Polity Press.
[2] Trucano, M. (2005). Knowledge maps: ICTs in education. infoDev / World Bank. https://www.infodev.org/infodev-files/resource/InfodevDocuments_8.pdf
[3] UNICEF. (2022). The state of global education: A path to recovery. UNICEF. https://www.unicef.org/reports/state-global-education-2022
[4] International Telecommunication Union. (2023). World telecommunication/ICT indicators database 2023 (27th ed.) [Data set]. ITU. https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx
[5] World Bank. (2022). Digital economy for Africa: Country diagnostic toolkit. World Bank Group. https://www.worldbank.org/en/programs/all-africa-digital-transformation
[6] OECD. (2020). The impact of COVID-19 on education: Insights from Education at a Glance 2020. OECD Publishing. https://doi.org/10.1787/69096873-en
[7] Haelermans, C. (2017). Digital tools in education: On usage, effects and the role of the teacher (Research Report No. 2017:01). Swedish Expert Group for Aid Studies.
[8] Malamud, O., & Pop-Eleches, C. (2011). Home computer use and the development of human capital. Quarterly Journal of Economics, 126(2), 987–1027. https://doi.org/10.1093/qje/qjr008
[9] Beuermann, D. W., Cristia, J., Cueto, S., Malamud, O., & Cruz-Aguayo, Y. (2015). One laptop per child at home: Short-term impacts from a randomized experiment in Peru. American Economic Journal: Applied Economics, 7(2), 53–80. https://doi.org/10.1257/app.20130267
[10] Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
[11] Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press.
[12] Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. National Bureau of Economic Research.
[13] Sen, A. (1999). Development as freedom. Oxford University Press.
[14] Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
[15] Bulman, G., & Fairlie, R. W. (2016). Technology and education: Computers, software, and the internet. In E. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 5, pp. 239–280). Elsevier. https://doi.org/10.1016/B978-0-444-63459-7.00005-1
[16] Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review (NBER Working Paper No. 23744). National Bureau of Economic Research. https://doi.org/10.3386/w23744
[17] Ferrini-Mundy, J., Mbwille, B., & McCrory, R. (2013). Examining the role of ICT in secondary school mathematics instruction in Tanzania. Journal of Education and Learning, 2(3), 1–14. https://doi.org/10.5539/jel.v2n3p1
[18] Asongu, S. A., & Nwachukwu, J. C. (2016). The role of ICT in reducing information asymmetry for financial access. Research in International Business and Finance, 38, 202–213. https://doi.org/10.1016/j.ribaf.2016.04.011
[19] Hjort, J., & Poulsen, J. (2019). The arrival of fast internet and employment in Africa. American Economic Review, 109(3), 1032–1079. https://doi.org/10.1257/aer.20161385
[20] Kenya National Examinations Council. (2023). KCSE examination report 2022. KNEC.
[21] West African Examinations Council. (2023). WASSCE chief examiner's report 2022. WAEC.
[22] World Bank. (2022). Living standards measurement study (LSMS) surveys [Data set]. https://www.worldbank.org/en/programs/lsms
[23] Tanzania National Examinations Council. (2023). CSEE 2022 examination statistics and chief examiner's report. NECTA.
[24] Kenya ICT Authority. (2022). Digital literacy programme annual report 2022. Ministry of ICT, Innovation and Youth Affairs, Kenya.
[25] TeleGeography. (2024). Submarine cable map [Data set]. PriMetrica. https://www.submarinecablemap.com
[26] Alliance for Affordable Internet. (2023). Affordability report 2023: The state of mobile internet connectivity. A4AI / Web Foundation. https://a4ai.org/affordability-report
[27] World Bank. (2022). Tracking SDG7: The energy progress report 2022 [Data set]. https://trackingsdg7.esmap.org
[28] Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer. https://doi.org/10.1007/b98835
[29] Stock, J. H., & Watson, M. W. (2020). Introduction to econometrics (4th ed.). Pearson.
[30] Baum, C. F., Schaffer, M. E., & Stillman, S. (2007). Enhanced routines for instrumental variables/GMM estimation and testing. Stata Journal, 7(4), 465–506. https://doi.org/10.1177/1536867X0800700402
[31] Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001
[32] Roth, J., Sant'Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What's trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218–2244. https://doi.org/10.1016/j.jeconom.2023.03.008
[33] Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277. https://doi.org/10.1016/j.jeconom.2021.03.014
[34] Krueger, A. B. (1999). Experimental estimates of education production functions. Quarterly Journal of Economics, 114(2), 497–532. https://doi.org/10.1162/003355399556052
[35] Hanushek, E. A. (2011). The economic value of higher teacher quality. Economics of Education Review, 30(3), 466–479. https://doi.org/10.1016/j.econedurev.2010.12.006
[36] Hennessy, S., D'Angelo, S., McIntyre, N., Koomar, S., Kreimeia, A., Cao, L., Brugha, M., & Zubairi, A. (2022). Technology use for teacher professional development in low- and middle-income countries: A systematic review. Computers and Education Open, 3, Article 100080. https://doi.org/10.1016/j.caeo.2022.100080
[37] GSMA. (2023). Connected women: The mobile gender gap report 2023. GSM Association. https://www.gsma.com/mobilefordevelopment/resources/mobile-gender-gap-report-2023
[38] World Bank / ESMAP. (2022). Mini grids for half a billion people: Market outlook and handbook for decision makers (Technical Report No. 014/22). Energy Sector Management Assistance Program, World Bank. https://doi.org/10.1596/37253
[39] UNESCO. (2021). No more excuses: Public policy to end gender-based discrimination in education. UNESCO Publishing. https://doi.org/10.54675/BGHK5136
[40] World Bank. (2018). World development report 2018: Learning to realize education's promise. World Bank Group. https://doi.org/10.1596/978-1-4648-1096-1
[41] International Telecommunication Union. (2022). Measuring digital development: Facts and figures 2022. ITU Publications. https://www.itu.int/en/ITU-D/Statistics/Documents/facts/FactsFigures2022.pdf
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 The Journal of Quality in Education
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
How to Cite
