intellectual tradition are particularly pronounced in the public health sector, where the WHO recommends using travel times instead of linear distances to calculate accessibility. Indeed, the WHO has developed the AccessMod geospatial analysis software to facilitate such calculations by public health researchers (Ray and Ebener 2008). Consequently, many public health researchers and professionals use travel times to assess the impact of accessibility on health care utilization (Buor 2003, Feikin _et al._ 2009). For example, Munoz and Kallestal (2012) demonstrate the relevance of travel time‐based accessibility measures to primary health care coverage and usage in Rwanda.

The economic development literature increasingly considers the role of accessibility thanks to the New Economic Geography championed by Krugman. Among many other things, Krugman suggested improving crude linear distance estimates to incorporate infrastructure quality and market demand (Krugman 1991). Agricultural economists and food security researchers in particular have focused on measuring the importance of market access to determining agricultural production, food prices and food security outcomes. A characteristic application is Baltenwick and Staal’s (2007) analysis of commodity spatial price formation in Kenya’s highlands, where they concluded that travel times to markets affect different commodities’ prices differently. For a helpful overview of such work see Chamberlin’s 2013 summary and for Nepal‐specific analysis see Jacoby (2000), Fafchamps and Hill (2005) and Thapa and Shiveley (2017).

Children’s limited mobility means accessible schools are believed essential to strong educational participation and outcomes. This belief is contested; in his 21‐country study Fillmer (2004) concludes that increased access has only a minor positive effect on enrollment. However, individual case studies differ. Lavy (1996) found that large travel times constrained educational outcomes in rural Ghana. Rolleston (2011) extends this analysis to find that improved education access significantly improves poverty rates in Ghana, albeit preferentially for economically privileged households. In Nepal, Shyam (2007) shows that geographic isolation affects school enrollment for primary and secondary school‐age children after controlling for other known determinants. He demonstrates that early childhood remoteness has a measurable effect on individuals’ lifetime educational performance even when accessibility to schools later improves.

# Measuring remoteness in Nepal

## Nepali context

Accessibility patterns in Nepal are heterogeneous. Altitudes stretch from roughly 70 meters above sea level in the Terai plains to well over 8,000 meters in the Himalayan mountains, with numerous smaller peaks and valleys falling between. Remoteness analysis is more applicable to the hills than the Terai, where the improved highways and flat terrain make access less of an issue. In the hills steep slopes make linear distance estimates meaningless and fast‐ moving rivers often prompt long detours, expanding travel times. In some remote mountainous districts, air transport is the only available travel modality other than walking, and outside of the Kathmandu Valley most areas are serviced by unreliable, expensive and irregular private bus services (World Bank 2017, Pokharel, R. _et al_ . 2015). For these reasons in 2012 the mean reported time to reach major market centers in rural areas was approximately 2 hours 15 minutes, despite the significant downward influence of households located in the flat and relatively well‐connected Terai (CBS 2011). Since only 17% of Nepal’s roughly 30 million inhabitants live in urban areas this implies heavy costs from remoteness on the country’s economy and society (CBS 2011).

Remoteness is a defining feature of rural life in Nepal due to its incredibly rough topography and diverse ecology and land cover. Yet most studies heavily abstract it, rely on estimates from surveys, or do not quantify it at all due to the complexity of accurately integrating so many different factors into a succinct measure. Data shortfalls pose an additional challenge, as key data sets are scattered across ministries or altogether missing. Even where roads data sets are available, the continued relevance of off‐road travel in rural areas frustrates typical network analysis.