# **2. Methods and data**

The RAI is calculated by overlaying a road network with a population layer, removing the urban areas, drawing a buffer around the roads, and counting the number of people in the buffer (World Bank, 2016). The code and documentation are available at <u>http://rai-wb.readthedocs.io/index.html.</u> We construct a GIS composed of a model of the distribution of rural population, and a geospatial model of rural roads (including their location and type). This first section describes the data that were used and the assumptions that were made to estimate rural accessibility with open source data.

## **2.1. Population and urban data**

We calculate rural accessibility for a subset of countries using different data sets for population distribution (WorldPop, LandScan, GHS-Pop) and urban area boundaries (Grump, EC LandScan and EC GHS). The results, displayed in Table 1, show a wide spread of values (RAI ranges from 29% to 51% for the Lao People’s Democratic Republic, depending on the choice of data sets, for example), and different rankings of countries depending on the data sets used. Overall, the RAI is lower when using WorldPop, and higher when using GHS-Pop. This is because WorldPop is the population data set with the least concentrated population in rural areas, while GHS-Pop is the most concentrated of those tested. The impact of the urban/rural area definition is very dependent on the country, though overall EC LandScan tends to lead to higher RAIs.

_Table 1 RAI calculated using the same roads but different population distribution datasets for a selection of countries with diverse geographies and population distribution_

|Population||WorldPop|||LandScan|||GHS-Pop||
|---|---|---|---|---|---|---|---|---|---|
|Urban/Rural limit|Grump|ECLandScan|EC GHS|Grump|ECLandScan|EC GHS|Grump|ECLandScan|EC GHS|
|Belize|27%|<br>45%|<br>41%|41%|<br>53%|<br>48%|35%|62%|<br>51%|
|Lao PDR|31%|<br>31%|<br>29%|39%|<br>33%|<br>36%|51%|49%|<br>45%|
|Panama|23%|<br>43%|<br>35%|34%|<br>37%|<br>35%|32%|49%|<br>35%|
|Sierra Leone|28%|<br>29%|<br>23%|31%|<br>29%|<br>25%|44%|46%|<br>33%|
|Nicaragua|30%|<br>41%|<br>37%|42%|<br>41%|<br>41%|55%|62%|<br>54%|
|Lesotho|20%|<br>21%|<br>21%|46%|<br>43%|<br>45%|43%|43%|<br>40%|
|Togo|30%|<br>26%|<br>23%|33%|<br>27%|<br>30%|54%|49%|<br>43%|
|Guinea|26%|<br>29%|<br>25%|27%|<br>25%|<br>23%|45%|44%|<br>37%|
|Benin|36%|<br>31%|<br>27%|45%|<br>36%|<br>37%|60%|59%|<br>59%|
|Kyrgyz Republic|25%|30%|31%|45%|45%|48%|54%|62%|66%|

We eventually chose WorldPop for our population layer. The computational process underlying the WorldPop data is fully transparent (Stevens et al., 2015), and the model is considered to be the most accurate and robust among the currently available data sets (World Bank, 2016). In addition, to maintain global comparability and methodological simplicity, we rely on the GRUMP (Global Rural-Urban Mapping Project) data set for urban-rural delimitation, as in (World Bank, 2016). Note that WorldPop uses roads as a factor to model population distribution, so there is an endogeneity in our analysis which could lead to an overestimation of rural accessibility. However, WorldPop is still the data set with the lowest concentration of population in rural areas and thus the one that gives the lowest rural accessibility in most countries (Table 1). These caveats call for high caution in the interpretation of the results presented here, and for further 3