combining the World Bank’s Service Delivery Indicators (SDI) on teacher absenteeism with geolocated data on schools (Bold et al., 2017). The reasons for absenteeism may vary, for example, if teachers in remote areas with fewer trained professionals are called on to perform a wider array of tasks in the community; this too could be examined by combining the SDI data with geolocated data. Understanding how to deliver technological infrastructure sustainably, however, requires not only geolocated data but also longitudinal data, which are limited in international education studies (McEwan, 2015). More research is thus needed to understand what school design and improvement can be implemented given limited access to building resources in rural areas, and how these vary as schools grow more rural, with decreasing access to urban centers.

Many teachers prefer to teach in urban areas, such that education systems offer financial or other incentives to entice teachers to rural areas (Crawfurd & Pugatch, 2020). Several studies have evaluated whether such programs boost teacher presence in rural schools (Chelwa, Pellicer, & Maboshe, 2019; Elacqua, Hincapie, Hincapié, & Montalva, 2019; Pugatch & Schroeder, 2014). However, the impacts of such a program—and thus its optimal design—may well be non-linear depending on the remoteness of the school and access to urban amenities.

Some challenges are very specific to education. For example, language ability of teachers is a distinct challenge in schools at different points along the urbanization spectrum. Increasing research shows that initial literacy instruction in a language with which students are already familiar (e.g., their native tongue) is most effective (Evans & Mendez Acosta, 2021; Piper, Zuilkowski, & Ong’ele, 2016). Another challenge is that of girls in rural areas having less access to menstrual hygiene management technology and private sanitation facilities compared to girls in urban areas, which can cause them to miss school during their menstrual periods (Adukia, 2017; Kazianga, Levy, Linden, & Sloan, 2013). With both examples, geolocated data together with household and industry surveys could be useful in assessing the incidence of these challenges along the rural-urban continuum and how to address them.

The third and final element to be considered is that of differences in labor demand along the urbanization continuum, which have heterogeneous effects on children’s decisions to attend school. Children are more likely to participate in school when there is an increase (perceived or real) in the educational skill premium, which is likely to be greater in urban areas due to access to more jobs that require differing levels of education, as they did after the introduction of call centers (Jensen, 2012) or the construction of paved roads connecting rural areas to nearby urban areas in India (Adukia, Asher, & Novosad, 2020). These perceptions – which are usually lower in rural areas – affect household investments in education. The urban–rural dichotomy is a poor indicator for this, since access to markets may vary more based on access to urban areas than on whether one is actually based in an urban area. New research can and should explore the reverberations of changes in the educational skill premium in urban centers as one moves further and further away from them, and how the effect of distance is mediated by the size of the urban center of reference.

Alternatively, if changes in labor demand increase the opportunity cost of attending schools, then educational participation can decrease. This happened, for example, after the introduction of manufacturing jobs in Mexico (Atkin, 2016) or an employment guarantee program in India (Adukia, 2020). Understanding the distinct opportunity costs related to school participation across the ruralurban continuum, and how curricular materials can be designed to match the skills and competencies necessary for an improved quality of life and motivate students to stay in school and succeed, is crucial.

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