Elsewhere, roads data present a different source of error. Chamberlin (2013) has noted that roads data sets in models are only “snapshots” of present conditions that fail to capture changing dynamics, a charge that applies to our analysis. This is problematic in Nepal, where even a casual observer will note that road quality changes frequently due to poor construction standards and maintenance practices. Deteriorating roads impact travel times, particularly during monsoon season and therefore we anticipate some error where road quality data are out of date.

All service facilities data sets were exclusive to Nepal and the lack of cross‐border facilities represents a possible source of error in border communities. However, these communities are either very small in the High Mountains, or usually already well served by Nepal‐based services in the Terai. So, the influence of border‐ based errors on aggregate numbers is likely minimal, though possibly locally impactful in some border municipalities. Care is advised when interpreting numbers from border municipalities with clear roads or paths to China or India.

We also acknowledge the importance of service quality, although our analysis ignores it for lack of data. Frequent staff absenteeism, inadequate supplies and/or poor quality services are common problems for schools, clinics and government offices in Nepal, particularly in remote areas, and impact usage patterns and development outcomes (RAP 2018, IDEA 2018). We therefore caution readers applying our findings in small‐ scale areas to research the impact of quality on usage of local services.

The assumptions listed above naturally impact the model’s accuracy, especially those regarding unencumbered travel and waiting for vehicles, both of which would slow down travel times in practice. Conversely, local residents tend to walk faster than visitors on local paths, somewhat offsetting these factors (RAP 2018). Users should remember that details of the local context like bus schedules and the frequency of landslides will cause departures from the model findings; we recommend careful analysis of such conditions when applying our findings on a small scale. Road‐blocking landslides in the monsoon and snowfall in the winter can have particularly dramatic effects on local transportation conditions.

# Model validation

## Consultations

We performed a thorough desk review of the model results to verify their general accuracy, consulting with peer organizations where necessary to ensure objective and comprehensive feedback. Our own knowledge of travel times in various locations was used to spot check the initial model results and make adjustments. After revisions, we consulted outside parties. The World Bank’s Far West Nutrition Program team provided us with detailed feedback on the accuracy of modeled travel times in the Far West using pre‐recorded point‐to‐point travel times from their trips to the region. Externally, we separately consulted with engineers and logistics specialists at the World Food Program’s Nepal Country Office and managers at the Rural Access Programme (RAP) to compare model travel times with actual travel times in areas where they work.

Based on these conversations, several adjustments to the draft models were made, for example reductions of walking path speeds and reductions of roadway status above 2,400 meters and in the Far West. Our consultations revealed that model travel times were usually 15‐20% too fast in most areas, especially over footpaths, and the model was consequently adjusted downwards. WFP logisticians also provided the principal inputs for monsoon season travel speed modifiers.

## Comparison to household survey data

Data from the “Nepal Household Risk Coping and Vulnerability Survey” (HRVS), a geo‐referenced nationwide survey, were used as a reference to estimate travel speeds and to validate model results. This survey randomly