Abstract
The present study provides the first evidence that illiteracy can be predicted from standard mobile phone logs. By deriving a broad set of novel mobile phone indicators reflecting users’ financial, social and mobility patterns this study addresses how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further it reveals how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than $1 trillion dollars each year. These results potentially enable cost-effective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chibba, M.: Financial inclusion, poverty reduction and the millennium development goals. Eur. J. Dev. Res. 21(2), 213–230 (2009)
IHSN: How (well) is Education Measured in Household Surveys? IHSN working paper 002 (2009)
Lokanathan, S., Lucas Gunaratne, R.: Behavioral insights for development from Mobile Network Big Data: enlightening policy makers on the State of the Art. (2014). SSRN 2522814
Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350(6264), 1073–1076 (2015)
Steele, J.E., Sundsøy, P., Pezzulo, C., Alegana, V., Bird, T., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.A., Iqbal, A., Hadiuzzaman, K., Lu, X., Wetter, E., Tatem, A., Bengtsson, L.: Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14(127), 20160690 (2017)
Sundsøy, P., Bjelland, J., Reme, B.A., Iqbal, A., Jahani, E.: Deep learning applied to mobile phone data for Individual income classification. In: ICAITA (2016)
Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., González, M.C., Lazer, D.: Tracking employment shocks using mobile phone data. J. R. Soc. Interface 12(107), 20150185 (2015)
Sundsøy, P., Bjelland, J., Reme, B.A., Jahani, E., Wetter, E., Bengtsson, L.: Estimating individual employment status using mobile phone network data. arXiv preprint arXiv:1612.03870 (2016)
Wesolowski, A., Qureshi, T., Boni, M.F., Sundsøy, P.R., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K., Buckee, C.O.: Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proc. Natl. Acad. Sci. 112(38), 11887–11892 (2015)
Lu, X., Wrathall, D.J., Sundsøy, P.R., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Canright, G.S., Engø-Monsen, K., Bengtsson, L.: Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen. Clim. Change 138(3–4), 505–519 (2016)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat., 1189–1232 (2001)
Eagle, N., Macy, M., Claxton, R.: Network diversity and economic development. Science 328(5981), 1029–1031 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sundsøy, P. (2017). Mitigating the Risks of Financial Exclusion: Predicting Illiteracy with Standard Mobile Phone Logs. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-60240-0_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60239-4
Online ISBN: 978-3-319-60240-0
eBook Packages: Computer ScienceComputer Science (R0)