sampled 6,000 households from 500 primary sampling units (PSU) nationwide.<sup>2</sup> The survey collected considerable locational information from households: GPS locations, names of villages and estimated average travel time and distance to markets, hospitals, banks, schools, and vehicle roads. To ensure the accuracy of the GPS data, this study employs data from three successive years from 2016 to 2018. For the purposes of this analysis GPS locations that fall outside the boundary of the listed village were eliminated from that year’s data set. This process narrowed the data set to 6,250 households from 6,367. If the GPS of more than a year fall within the boundary, we compute the household location as the geographic average of the GPS data.

Table 7 summarizes the mean and median time to the facilities based on HRVS result and the developed model (regular and monsoon). To comparing the modeled remoteness with the survey results, households with travel times over 8 hours are dropped, expecting that people will not walk over 8 hours per day and will take a long rest overnight, where the time for resting is not captured in the model. Roads are most accessible to households, while banks are least accessible. The final model results for the location of a household GPS point were spatially joined to the HRVS data and regressed against reported times to validate the final results. Figure 7 plots the regression results.

**_Table 7: Comparison of model times to Reported travel times (HRVS)_**

||**Ro**|**ad**_(excludin_|_g path)*_|**M**|**edical Faci**|**lities**||**Bank**||
|---|---|---|---|---|---|---|---|---|---|
||**HRVS**|**Normal**|**Monsoon**|**HRVS**|**Normal**|**Monsoon**|**HRVS**|**Normal**|**Monsoon**|
|**Mean Time**_(min)_|30.18|28.62|32.94|38.88|39.9|44.52|86.4|62.4|67.2|
|**Median Time**<br>_(min)_|10.02|9.54|10.92|25.02|20.82|22.2|45|28.2|29.52|

_* Road excluded path and VR, as the HRVS questionnaire asked about time to drivable or black‐topped road_ The results show a rough but inexact convergence between reported travel times and model results. This can also be seen when comparing the sample charts of provincial aggregates in Figures 8 and 9. Our analysis is that this reflects both model and survey error. As alluded to above, there are known localized shortfalls in the quality and completeness of our model input data that present unavoidable sources of error. These are most notable for bridge crossings, road categories and conditions, and some service locations (e.g. private health facilities). Unfortunately, gathering the outstanding data at a sufficient level of detail was impossible given the uneven conditions of data generation and sharing in Nepal.

2 The HRVS sample frame was all households in non‐metropolitan areas per the 2011 Census definition, excluding households in the Kathmandu valley (Kathmandu, Lalitpur and Bhaktapur districts). The country was segmented into 11 analytical strata, defined to correspond to those used in the Nepal Living Standards Survey (NLSS‐III: excluding the three urban strata used there). To increase the concentration of sampled households, 50 of the 75 districts in Nepal were selected with probability proportional to size (the measure of size being the number of households). PSUs were selected with probability proportional to size from the entire list of wards in the 50 selected districts, one stratum at a time. 5,835 households out of 6,000 in the survey 2016 were re‐ surveyed in 2017 and an additional 170 households were surveyed only in 2017. Similarly, an additional 197 households were surveyed in 2018 only.