Access to electricity and digital inclusion: evidence from mobile call detail records

Abstract

Despite significant progress in mobile connectivity, a large number of individuals in Sub-Saharan Africa are at risk of being left behind, especially those living in rural areas and women. In this paper, we use an original data set of mobile call detail records from Senegal and exploit variations across plausibly similar rural municipalities to assess the impact of access to electricity on mobile connectivity. We find that access to mobile connectivity for rural users, measured by mobile telephony subscriptions or smartphone ownership, increases with access to electricity, with a stronger impact for women. The impact on usage is also positive, but stems from the increased volume of incoming communications: rural mobile users with access to electricity do not place more calls than those with limited access; rather, they receive more calls and text messages from urban areas, especially those with higher living standards. These findings suggest that access to electricity can be a significant driver of digital inclusion in Sub-Saharan Africa.

Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa

“…Women may be less likely than men to own mobile phones due to receiving less education (which results in lower levels of literacy and digital literacy), higher levels of poverty, and cultural norms of patriarchy that prevent female empowerment and reduce the agency of women ( GSMA, 2022 ; LeFevre et al, 2020 ; Marron et al, 2020 ; Paradigm Initiative, 2020 ; Porter et al, 2020 ). The rural population may be less likely than urban residents to own mobile phones due to many of the same factors (fewer years of schooling, lower levels of literacy and digital literacy, and higher levels of poverty; GSMA, 2020 ; Houngbonon et al, 2021 ; Wesolowski et al, 2012 ). However, the main driver of the inequality may be that there is less infrastructure in rural areas than in urban areas ( Krönke, 2020 ; Krönke et al, 2022 ); rural areas have lower cell coverage, a less reliable source of electricity, and often lack high-speed broadband ( Houngbonon et al, 2021 ).…”

“…The rural population may be less likely than urban residents to own mobile phones due to many of the same factors (fewer years of schooling, lower levels of literacy and digital literacy, and higher levels of poverty; GSMA, 2020 ; Houngbonon et al, 2021 ; Wesolowski et al, 2012 ). However, the main driver of the inequality may be that there is less infrastructure in rural areas than in urban areas ( Krönke, 2020 ; Krönke et al, 2022 ); rural areas have lower cell coverage, a less reliable source of electricity, and often lack high-speed broadband ( Houngbonon et al, 2021 ). Not surprisingly, we found that wealthy individuals are more likely to own mobile phones than less wealthy individuals; this effect was exacerbated when considering SPs relative to BPs.…”

Power Generation Infrastructure and its Effect on Electric Energy Consumption: Context in Indonesia, 2013–2020

“…Then, the distribution of electricity by the incompetent State Electricity Company (PLN) based on homeownership and territorial status, causes inequality in household electricity access in rural areas (Zuhri et al, 2020). Houngbonon et al (2021) focuses on rural populations in Sub-Saharan Africa who tend to suffer from limited electricity connectivity, which risks disrupting mobile phone subscriptions. In a similar dimension, empowering access to electricity has changed socio-economic conditions in Africa (Andrade-Pacheco et al, 2019).…”

Aggregated Traffic Anomaly Detection Using Time Series Forecasting on Call Detail Records

“…Based on equation ( 5), the odd ratio of success to failure will be Euler's number to the power of coefficients of the fitted model [30][31][32][33].…”