Kristjanson et al. 2005). This reflects the importance of infrastructure but may obscure social aspects of inaccessibility, like women’s avoidance of overnight travel. Less common are composite indices built off a combination of the above (Babu et al. 2014).

Reported travel times to specific services from survey instruments, sometimes further divided by travel modality, are very popular measures of accessibility among economists (Babu et al. 2014, Jacoby 2000, Minten 1999).

Questions regarding travel times are common to Living Standards Measurement Surveys promulgated by the World Bank and hence data are available for many countries. Despite their popularity, reported travel times contain serious inaccuracies, biases and limitations. Respondents may misreport times (a.k.a. recall bias) and idiosyncratic household conditions (i.e. disabilities, physical fitness, schedules of nearby bus) create many outliers (Roberts et al. 2006). Assessing Nepal’s 1996 Living Standards Measurement Survey, Jacoby (2000) notes that reported travel times within wards vary widely around the ward median value for these reasons. Similarly, when comparing reported travel times to a locally validated cost time model, Ahlström et al. (2011) found responses differed by up to 30% (+/‐) of the mean modeled travel time for a given district. These findings imply that while such data are appropriate for household level analysis, aggregation and generalization from them is problematic. Reported times also do not work for assessing new or planned infrastructure and are prohibitively expensive to collect at scale.

Some researchers employ road network analysis within a GIS to calculate travel times along roadways (Delgado and Baltenwick 2000). Such analysis work well where road network data sets are complete, terrain’s impact is easy to model and off‐road travel is insignificant. Only a few researchers have calculated their own cost time models in a GIS. Notable examples of such models include Kosmidou‐Bradely and Blankespoor’s national mobility model for Afghanistan (2019), the continent‐scale analysis of HarvestChoice in Africa (2016) and the district‐scale, locally validated analysis of Ahlström et al. (2011) in Sri Lanka. The former model is particularly similar to ours in terms of context, scale and design and worthy of study by those interested. Both cost time models and reported travel times can be used to compute “economic distances” of financial cost for traveling a given distance, for instance the cost of using transport plus the opportunity cost of time (Chamberlin 2013).

A notable weakness of almost all research, perpetuated here for lack of manageable alternatives, is the tendency to calculate accessibility in terms of the single nearest service location, instead of multiple services (Chamberlin 2013). Transportation researchers’ more sophisticated models handle multiple destinations better but at the cost of communicative and analytical simplicity. We favor the simple approach in constructing our model but acknowledge the artificial limitation it imposes and invite further research in this regard.

# Accessibility and Development

Remoteness plays a heavy role in human and economic development. Jalan speaks of a “geographic poverty trap” wherein a lack of accessibility perpetuates poverty (Jalan, J. et al. 2002). For example, in several case studies Bird, K. _et al._ (2002) highlight the strong prevalence of chronic poverty in rural areas isolated by distance and/or ecology.

This is particularly so in South Asia, where the UNDP’s Human Development Report 2016 states that the population in multidimensional poverty is much higher in rural areas (64%) than urban areas (25%), compared to 29% and 11% globally. In India each additional 10 km from a town is associated with a 3.2% reduction in mean earnings (Asher, S. et al. 2016). In Nepal itself Sapkota (2017) finds that remote, rural villages have higher poverty levels and report lower levels of health, education and happiness after controlling for household fixed effects.

Health, education and market development researchers and practitioners have long recognized the determining role of physical access in conditioning development outcomes. Practical implementations of this