2009; Bryceson et al., 2008), local market characteristics (Casaburi et al., 2013), and lack of agglomeration economies and/or human capital (Asher et al., 2018).

Given this contextual uncertainty, evidence exists that the most consistent living standards improvements from rural roads are not economic in nature but instead linked to improved access to services such as health (Van de Walle and Cratty, 2002), finance (Binswanger et al., 1993), education (Adukia et al., 2020; Kristjanson et al., 2005), or administration. Comparing Asian Development Bank projects in the Philippines, Indonesia, and Sri Lanka, Hettige (2006) finds that rural roads most consistently benefit the poor through improved access to services, whereas poverty reduction is conditional on the contextual factors listed above or complementary investments.

The relationship between rural accessibility and human development is pronounced in South Asia compared to global norms. Whereas 29% and 11% of rural and urban populations respectively live in multidimensional poverty globally, in South Asia 64% and 25% do because of the excessive concentration of economic opportunity in urban areas (Asher et al., 2018; United Nations Development Programme, 2017). In India, increasing distance from towns and their services correlates with reduced household earnings (Asher et al., 2018) and improving rural road connectivity increases rural household consumption and reduces rural poverty (Rao and Jayasree, 2003; Khandker and Koolwal, 2011). In Nepal, Jacoby (2000) and Fafchamps and Shilpi (2003) demonstrated that income, cropping intensity, access to non-farm employment, and education levels in rural areas progressively fall as travel times to markets increase. In a small-scale study of 3 Nepali village development committees, Sapkota (2018) found that households with longer travel times to services have higher poverty levels and lower levels of health, education, and reported happiness. Shrestha (2012) found that a 1% increase in travel times to paved roads decreased rural farmland values and implied profits by 0.25%.

# 2.2. Measures of accessibility

Measuring accessibility baselines and improvements is critical to establishing rural roads’ benefits. Simple metrics like linear distance, densities of roads and services, or reported travel times in household surveys are easy to compute or parse from existing data sources but are often difficult to scale, only tangentially related to access, influenced by idiosyncratic survey responses (Ahlstr ̈om et al., 2011), or inaccurate in mountainous areas (Huber, 2015). Nevertheless, these are commonly used in economists’ evaluations of rural roads: for instance, Banerjee et al. (2012) and Thapa and Shively (2017) measure accessibility in terms of road length density per administrative area in China and Nepal, respectively.

Planners and researchers looking for greater accuracy and scalability will typically employ geospatial models of accessibility. The foundational models employed are network and cost-time models, which may be further elaborated in gravity models or measures of geographic and potential accessibility (Rodrigue, 2016). Network analysis calculates the anticipated travel time between one or many origins and destinations over an integrated transportation network. Transportation networks can take the form of roads, paths, railways, waterways, flights, or combinations thereof. Speeds are assigned to segments of this network which are used to calculate travel times between origin-destination pairs, with adjustments for impedances like traffic or border crossings. Implementations of network analysis range from the study of P ́aez (2004) on network accessibility’s relationship to economic activity in East Asia, the study of intra-city accessibility in Colombia by Sabogal et al. (2018), World Bank studies of accessibility to jobs in Cox’s Bazar Bangladesh (World Bank, 2020), and even Google Maps’s Directions tool.

Geospatial cost-time models represent travel in terms of the minimum time to cross each cell in a grid (raster). These times are calculated based on some combination of the underlying roads and road speeds, landcover, walking speeds, terrain, and other factors (Zhu et al., 2016). The advantage of this approach is that grid cells can model travel as on-network, off-network (e.g. walking), or as a combination of the two (walk to the road and then take a bus). Relevant examples leveraging these off-road capabilities to analyze access in difficult contexts include the analysis of Kosmidou-Bradley and Blankespoor (2019) on accessibility to services in rural Afghanistan, the study by Ahlstr ̈om et al. (2011) on poverty’s relationship to accessibility in southern Sri Lanka, models of accessibility to markets in Africa by Harvest Choice and International Food Policy Research Institute (IFPRI) (2016), and global-scale analysis of wealth stratification by accessibility to urban centers by Weiss et al. (2018).

The accuracy with which models reflect seasonal access changes depends on the model and metrics employed. Transport geographers (Jafino et al., 2020; Rodrigue et al., 2016; Jenelius, 2009) and civil engineers 5